A question answer system answers questions posed in a natural language format by applying advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies. Question answer systems differ from typical document search technologies because document search technologies generate a list of documents ranked in order of relevance based on a word query, whereas question answer systems analyze contextual details of a question expressed in a natural language and provide a precise answer to the question.
System developers typically train question answer systems for specific domains to provide relevant answers to domain-specific questions (e.g., financial domain, medical domain, travel domain, etc.). One approach to train a question answer system is for a set of experts to input detailed domain training knowledge into a knowledge base that, in turn, the question answer system utilizes to answer questions. Another approach to training a question answer system is to ingest a large set of corpora from trusted, traditional sources (textbooks, journals, etc.) into the question answer system's knowledge base, which the question answer system utilizes to answer questions.
A question answer system's ability to answer a question depends upon whether facts corresponding to the answer are located in the knowledge base. If facts do not exist in the knowledge base to answer a question, the question answer system may not be able to discover enough evidence to support precise answers to the question.